Merge remote-tracking branch 'origin/main' into feat/support-agent-sandbox

api/tests/integration_tests/vdb/qdrant/test_qdrant.py

@@ -1,3 +1,5 @@
+import uuid
+
 from core.rag.datasource.vdb.qdrant.qdrant_vector import QdrantConfig, QdrantVector
 from core.rag.models.document import Document
 from tests.integration_tests.vdb.test_vector_store import (
@@ -18,6 +20,10 @@ class QdrantVectorTest(AbstractVectorTest):
                 api_key="difyai123456",
             ),
         )
+        # Additional doc IDs for multi-keyword search tests
+        self.doc_apple_id = ""
+        self.doc_banana_id = ""
+        self.doc_both_id = ""
 
     def search_by_vector(self):
         super().search_by_vector()
@@ -27,6 +33,77 @@ class QdrantVectorTest(AbstractVectorTest):
         )
         assert len(hits_by_vector) == 0
 
+    def _create_document(self, content: str, doc_id: str) -> Document:
+        """Create a document with the given content and doc_id."""
+        return Document(
+            page_content=content,
+            metadata={
+                "doc_id": doc_id,
+                "doc_hash": doc_id,
+                "document_id": doc_id,
+                "dataset_id": self.dataset_id,
+            },
+        )
+
+    def setup_multi_keyword_documents(self):
+        """Create test documents with different keyword combinations for multi-keyword search tests."""
+        self.doc_apple_id = str(uuid.uuid4())
+        self.doc_banana_id = str(uuid.uuid4())
+        self.doc_both_id = str(uuid.uuid4())
+
+        documents = [
+            self._create_document("This document contains apple only", self.doc_apple_id),
+            self._create_document("This document contains banana only", self.doc_banana_id),
+            self._create_document("This document contains both apple and banana", self.doc_both_id),
+        ]
+        embeddings = [self.example_embedding] * len(documents)
+
+        self.vector.add_texts(documents=documents, embeddings=embeddings)
+
+    def search_by_full_text_multi_keyword(self):
+        """Test multi-keyword search returns docs matching ANY keyword (OR logic)."""
+        # First verify single keyword searches work correctly
+        hits_apple = self.vector.search_by_full_text(query="apple", top_k=10)
+        apple_ids = {doc.metadata["doc_id"] for doc in hits_apple}
+        assert self.doc_apple_id in apple_ids, "Document with 'apple' should be found"
+        assert self.doc_both_id in apple_ids, "Document with 'apple and banana' should be found"
+
+        hits_banana = self.vector.search_by_full_text(query="banana", top_k=10)
+        banana_ids = {doc.metadata["doc_id"] for doc in hits_banana}
+        assert self.doc_banana_id in banana_ids, "Document with 'banana' should be found"
+        assert self.doc_both_id in banana_ids, "Document with 'apple and banana' should be found"
+
+        # Test multi-keyword search returns all matching documents
+        hits = self.vector.search_by_full_text(query="apple banana", top_k=10)
+        doc_ids = {doc.metadata["doc_id"] for doc in hits}
+
+        assert self.doc_apple_id in doc_ids, "Document with 'apple' should be found in multi-keyword search"
+        assert self.doc_banana_id in doc_ids, "Document with 'banana' should be found in multi-keyword search"
+        assert self.doc_both_id in doc_ids, "Document with both keywords should be found"
+        # Expect 3 results: doc_apple (apple only), doc_banana (banana only), doc_both (contains both)
+        assert len(hits) == 3, f"Expected 3 documents, got {len(hits)}"
+
+        # Test keyword order independence
+        hits_ba = self.vector.search_by_full_text(query="banana apple", top_k=10)
+        ids_ba = {doc.metadata["doc_id"] for doc in hits_ba}
+        assert doc_ids == ids_ba, "Keyword order should not affect search results"
+
+        # Test no duplicates in results
+        doc_id_list = [doc.metadata["doc_id"] for doc in hits]
+        assert len(doc_id_list) == len(set(doc_id_list)), "Search results should not contain duplicates"
+
+    def run_all_tests(self):
+        self.create_vector()
+        self.search_by_vector()
+        self.search_by_full_text()
+        self.text_exists()
+        self.get_ids_by_metadata_field()
+        # Multi-keyword search tests
+        self.setup_multi_keyword_documents()
+        self.search_by_full_text_multi_keyword()
+        # Cleanup - delete_vector() removes the entire collection
+        self.delete_vector()
+
 
 def test_qdrant_vector(setup_mock_redis):
     QdrantVectorTest().run_all_tests()